[HTML][HTML] AI augmented Edge and Fog computing: Trends and challenges
In recent years, the landscape of computing paradigms has witnessed a gradual yet
remarkable shift from monolithic computing to distributed and decentralized paradigms such …
remarkable shift from monolithic computing to distributed and decentralized paradigms such …
Energy efficient task scheduling based on deep reinforcement learning in cloud environment: A specialized review
The expanding scale of cloud data centers and the diversification of user services have led
to an increase in energy consumption and greenhouse gas emissions, resulting in long-term …
to an increase in energy consumption and greenhouse gas emissions, resulting in long-term …
Deep reinforcement learning for energy and time optimized scheduling of precedence-constrained tasks in edge–cloud computing environments
The wide-spread embracement and integration of Internet of Things (IoT) has inevitably lead
to an explosion in the number of IoT devices. This in turn has led to the generation of …
to an explosion in the number of IoT devices. This in turn has led to the generation of …
A fair, dynamic load balanced task distribution strategy for heterogeneous cloud platforms based on Markov process modeling
Load balancing techniques in cloud computing can be applied at three different levels:
Virtual machine load balancing, task load balancing, and resource load balancing. At all …
Virtual machine load balancing, task load balancing, and resource load balancing. At all …
[HTML][HTML] Multi-objective task scheduling optimization in cloud computing based on fuzzy self-defense algorithm
X Guo - Alexandria Engineering Journal, 2021 - Elsevier
A cloud computing multi-objective task scheduling optimization based on fuzzy self-defense
algorithm is proposed. Select the shortest time, the degree of resource load balance and the …
algorithm is proposed. Select the shortest time, the degree of resource load balance and the …
Reinforcement learning-based application autoscaling in the cloud: A survey
Reinforcement Learning (RL) has demonstrated a great potential for automatically solving
decision-making problems in complex, uncertain environments. RL proposes a …
decision-making problems in complex, uncertain environments. RL proposes a …
[HTML][HTML] An IoT-based resource utilization framework using data fusion for smart environments
Nowadays, many communities are emerging towards smart environments, requiring the
communication and collaboration of diverse Internet-of-Things (IoT) devices. A smart …
communication and collaboration of diverse Internet-of-Things (IoT) devices. A smart …
Deep reinforcement learning for application scheduling in resource-constrained, multi-tenant serverless computing environments
Serverless computing has sparked a massive interest in both the cloud service providers
and their clientele in recent years. This model entails the shift of the entire matter of resource …
and their clientele in recent years. This model entails the shift of the entire matter of resource …
Multiobjective Edge Server Placement in Mobile-Edge Computing Using a Combination of Multiagent Deep Q-Network and Coral Reefs Optimization
A Asghari, MK Sohrabi - IEEE Internet of Things Journal, 2022 - ieeexplore.ieee.org
The growth of telecommunication technologies, especially 5G, the growing popularity of
smart mobile devices, the emergence of smart cities and Internet of Things (IoT), and the …
smart mobile devices, the emergence of smart cities and Internet of Things (IoT), and the …
Multi-objective edge server placement using the whale optimization algorithm and game theory
Due to the users' mobility, new online applications, as well as low processing power and
limited energy of smart devices, traditional cloud computing models could not provide new …
limited energy of smart devices, traditional cloud computing models could not provide new …